def __init__(self, frame, window, init_frames_count = 20):
self.buffer = [cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)]
self.position = Position(self.buffer, *window)
self.learning_component = LearningComponent(self.position.calculate_patch())
self.detector = Detector(self.position, self.learning_component)
self.tracker = Tracker(self.position)
self.is_visible = True
self.integrator = Integrator(self.learning_component)
self.init_frames_count = init_frames_count
self.detected_windows = None
self.tracked_window = None
class TLD_IVMIT:
def __init__(self, frame, window, init_frames_count = 20):
self.buffer = [cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)]
self.position = Position(self.buffer, *window)
self.learning_component = LearningComponent(self.position.calculate_patch())
self.detector = Detector(self.position, self.learning_component)
self.tracker = Tracker(self.position)
self.is_visible = True
self.integrator = Integrator(self.learning_component)
self.init_frames_count = init_frames_count
self.detected_windows = None
self.tracked_window = None
def start(self, frame):
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if self.init_frames_count == 0:
start = time()
self.tracked_window = self.tracker.track(frame, self.position)
self.buffer[0] = frame
print "Tracking:", time()- start
start = time()
self.detected_windows = self.detector.detect(self.position, self.tracked_window is not None)
print "Detected windows count:", len(self.detected_windows)
print "Detection:", time()- start
start = time()
# filtered_detected_windows = [(window, patch, proba) for window, patch, proba in self.detected_windows if proba > 0.7]
single_window, self.is_visible = self.integrator.get_single_window(self.position, self.detected_windows, self.tracked_window)
print "Integration:", time()- start
if self.is_visible:
self.position.update(*single_window)
# start = time()
# self.learning_component.n_expert()
# self.learning_component.p_expert()
# print "Update training set:", time()- start
else:
self.tracked_window = self.tracker.track(frame, self.position)
self.buffer[0] = frame
if self.tracked_window is not None:
i = 0
while i < 5:
self.position.update(x=np.random.randint(0,self.buffer[0].shape[1]-self.position.width))
if self.position.is_correct() and windows_intersection(self.position.get_window(), self.tracked_window) == 0:
self.learning_component.update_negatives(self.position.calculate_patch())
i += 1
self.position.update(*self.tracked_window)
self.learning_component.update_positives(self.position.calculate_patch())
self.init_frames_count -= 1
else:
self.init_frames_count = 0
self.is_visible = False
return self.position
